from sklearn.model_selection import train_test_split from sklearn.datasets import load_iris from sklearn.neighbors import KNeighborsClassifier # Load iris file iris = load_iris() iris.keys() print(f"Target names: \n {iris.target_names} ") print(f"\n Features: \n {iris.feature_names}") # Train set e Test set X_train, X_test, y_train, y_test = train_test_split( iris["data"], iris["target"], random_state=4 ) # KNN knn = KNeighborsClassifier(n_neighbors=1) knn.fit(X_train, y_train) # new array to test X_new = [[1, 2, 1, 4], [2, 3, 4, 5]] prediction = knn.predict(X_new) print( "\nNew array: \n {}" "\n\nTarget Names Prediction: \n {}".format(X_new, iris["target_names"][prediction]) )